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This is a Experimental Study study.

September 16, 2025SymmetryOpen Access

Research on Fault-Diagnosis Technology of Rare-Earth Permanent Magnet Motor Based on Digital Twin

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Proposes a diagnostic framework that classifies bearing faults in motors based on vibration signal asymmetries, suggesting high effectiveness.

The model achieved a high average accuracy of 98.44% for detecting bearing faults, demonstrating impressive diagnostic capabilities.
Using a digital twin, the study effectively simulated symmetrical and asymmetrical states to improve fault detection methodologies.
An optimized VMD algorithm was applied to extract fault features, which were then classified using a CNN-BiLSTM model for precision.
Validation on a real-world dataset confirmed the method's robustness, achieving over 95% accuracy in fault identification.

Ma et al. (2025) studied this question.

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Proposes a diagnostic framework that classifies bearing faults in motors based on vibration signal asymmetries, suggesting high effectiveness.